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Impact of IT Capability on Inventory management: An Empirical Study

Procedia Computer Science 2022年 199卷 142-148页

作者： Xin Tian Haoqing Wang Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Despite the widespread adoption of technology in inventory management, empirical research that examines the effect of these technologies is scarce. Does IT capability really have a positive effect on the inventory management? This paper empirically studies the usage of information technology by the definition of IT capability. Our unique panel data set allows us to explore the relationship between technology usage and inventory management. Our results show that IT capability has a positive effect on the inventory strategy and inventory operation process, and has a negative effect on the out-of-stock level. Our research demonstrates the superiority of the usage of technology.

关键词： intelligent warehousing business intelligence inventory management logistics

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Value of High-Quality Distribution in Front Warehouse Mode Retailing

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Procedia Computer Science 2022年 199卷 110-117页

作者： Jiayi Zhu Xin Tian Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China

Front warehouse mode retailing originates from the e-commerce logistics industry, pre-positioning the warehouse and placing customer orders close to the community service station, so that goods can be delivered quickly. Consumers have put forward higher requirements for the timeliness of logistics distribution and the convenience of online purchases in the front warehouse mode. Using panel data of 37,362 transactions from 159 front warehouses in the online market, we study how consumers’ satisfaction on products and logistics, as well as the punctuality of sorting and delivery affect sales performance. Our results show that negative review of products and logistics will significantly reduce retail sales performance. Interestingly, we find that punctual sorting and delivery increase the number of daily orders while reduce the average sales amount per order. When the punctuality of delivery service is guaranteed, consumers are willing to place orders more frequently even for some low-priced daily goods. This paper emphasizes the importance of the quality of delivery service in the front warehouse mode, and inspires retailers to give full play to the role of the front warehouse in meeting the daily needs of consumers for high-frequency and fast-moving consumer goods.

关键词： front warehouse delivery service customer satisfaction sales performance retailing

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Review of graph construction and graph learning in stock price prediction

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Procedia Computer Science 2022年 214卷 771-778页

作者： Yunong Wang Yi Qu Zhensong Chen Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Management and Engineering Capital University of Economics and Business Beijing 100070 China

Precise prediction of stock prices leads to more profits and more effective risk prevention, which is of great significance to both investors and regulators. Recent years, various kinds of information not directly-relevant with stock prices have received more attention, such as texts, images or connections. These external information has the potential of reflecting or influencing fluctuations, and thus, given the utilization of advanced analyzing techniques, the forecasting performance of stock prices could be promoted substantially. For instance, graph neural network models have expanded into many other disciplines including stock price prediction, and exhibited impressive representation learning ability. However, in stock markets, well-defined graphs are rarely seen and how to formulate the graph structures needed remains a challenging problem. Towards this end, this article presents a comprehensive overview of graph construction and graph learning in stock price prediction, by reviewing the existing studies, summarizing its general paradigm, special cases and proposing possible prospects. Our research not only systematically reveals the feasible ways of constructing graphs in financial markets, but also provides insights for further implementations of graph learning models into stock prediction tasks.

关键词： Stock price prediction Graph construction Graph learning data structure

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The asset allocation utility of digital cryptocurrency

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Procedia Computer Science 2022年 214卷 1174-1181页

作者： Wen Long Maohua Wang Man Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100090 China Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining & Knowledge Management Chinese Academy of Sciences Beijing 100190 China

In recent years, the digital cryptocurrency market has witnessed rapid development, but its asset allocation function has yet to be verified. In this paper, DCC-GARCH model is used to estimate the dynamic correlation between digital cryptocurrency and current mainstream assets, and the corresponding hedging effect of digital cryptocurrency is studied. Then, three mainstream asset allocation strategies are selected to analyze the risk and return of asset allocation portfolio after adding digital cryptocurrency, which verifies the asset allocation utility of digital cryptocurrency. This study can provide investment basis for investors and has certain theoretical and practical significance.

关键词： Digital Currency Hedging Effect Asset Allocation DCC-GARCH Model

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Multiple factor flows and heterogeneous premium of urban house prices: Empirical evidence from Chinese cities

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Finance Research Letters 2024年 69卷

作者： Huang, Xirong Geng, Chen Li, Xiuting Dong, Jichang School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation at UCAS Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences es Beijing 100190 China

Notable Regional heterogeneity is commonly observed in housing markets. This study examines how multiple factor flows affect urban house prices and the resulting premium. It utilizes over 100 million pieces of Chinese intercity factor flow data to construct a multi-layer network and introduces a new metric called multiple connectivity quality to measure a city's comprehensive factor connection capability. Empirical analysis reveals that cities with higher multiple connectivity quality tend to exhibit higher price premiums, making them more resilient to market fluctuations. This study offers fresh insight into regional heterogeneity within housing markets and holds practical significance for stabilizing these markets. © 2024 The Author(s)

关键词： Heterogeneous premiums House prices Multi-layer network Multiple factor flows Panel data model

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Latest Technologies on dataset Distillation: A Survey

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Procedia Computer Science 2024年 242卷 1112-1117页

作者： Muyang Li Yi Qu Yong Shi Research Center on Fictitious Economy and Data Science Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska at Omaha Omaha NE 68182 USA

dataset distillation refers to the process of constructing a smaller dataset based on a larger dataset, so that the training model with the smaller dataset can obtain similar results to the larger dataset. In this paper, we first briefly review the basic methods and some recent advances in this field from the perspectives of meta-learning framework and data matching framework, including the backpropagation through time(BPTT), kernel ridge regression(KRR), decoupled techniques, gradient matching(DC), trajectory matching(MTT) and distribution matching(DM). The results of the plug-and-play approach in recent years are then presented. Finally, we discuss possible future developments from the perspectives of data distillation algorithms on large datasets, the relationship between compress ratio and information contained, more tasks and data modalities, and the use of generative models to improve the distillation results of datasets.

关键词： dataset Distillation Survey Meta-learning framework data matching framework

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Impact of Consumer Perceived Characteristics on Fruit Sales: Evidence from Community Group-buying

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Procedia Computer Science 2024年 242卷 662-668页

作者： Hailiang Jiang Xin Tian Ye Tao Lulu Wang Research Center on Fictitious Economy and Data Science. Chinese Academy of Sciences Beijing 100190 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China Beijing HAOLINJU CVS Co. Ltd Beijing 100088 China Shanghai HEADING Information Engineering Co. Ltd. Shanghai 201112 China

There is a growing trend among retailers to sell fruits through their community group-buying (CGB) channels. Based on real operational data, we employ econometric models to empirically analyze how consumer perception characteristics in CGB affect fruit sales. Our findings suggest that incorporating subjective experiences into product descriptions can positively impact fruit sales for retailers; fresh produce stores outperform regular stores in fruit sales of CGB; Dark-colored fruits sell more than light-colored fruits in the CGB channel. Our study offers valuable insights for retailers selling fruits through CGB channels.

关键词： community group-buying subjectivity fresh produce stores dark-colored fruits

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Size-Invariance Matters: Rethinking Metrics and Losses for Imbalanced Multi-object Salient Object Detection 41

Size-Invariance Matters: Rethinking Metrics and Losses for I...

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41st International Conference on Machine Learning, ICML 2024

作者： Li, Feiran Xu, Qianqian Bao, Shilong Yang, Zhiyong Cong, Runmin Cao, Xiaochun Huang, Qingming Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China Institute of Information Science Beijing Jiaotong University Beijing China School of Control Science and Engineering Shandong University Jinan China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China School of Cyber Science and Tech. Sun Yat-sen University Shenzhen Campus China Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing China

This paper explores the size-invariance of evaluation metrics in Salient Object Detection (SOD), especially when multiple targets of diverse sizes co-exist in the same image. We observe that current metrics are size-sensitive, where larger objects are focused, and smaller ones tend to be ignored. We argue that the evaluation should be size-invariant because bias based on size is unjustified without additional semantic information. In pursuit of this, we propose a generic approach that evaluates each salient object separately and then combines the results, effectively alleviating the imbalance. We further develop an optimization framework tailored to this goal, achieving considerable improvements in detecting objects of different sizes. Theoretically, we provide evidence supporting the validity of our new metrics and present the generalization analysis of SOD. Extensive experiments demonstrate the effectiveness of our method. The code is available at https://***/Ferry-Li/SI-SOD. Copyright 2024 by the author(s)

关键词： Object detection

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COVID-19 Impact on Global Electricity Generation Structure-Based on Sustainable Development Perspective

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Procedia computer science 2022年 214卷 1206-1213页

作者： Shuyang Peng Xian Yang Haibo Lu Kun Guo School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China. Research Center on Fictitious Economy & Data Science Chinese Academy of Sciences Beijing 100190 China. Key Laboratory of Big Data Mining and Knowledge Management Chinese Academy of Sciences Beijing 100190 China.

This paper has sorted out the general logic of the impact of COVID-19 on energy consumption. In the short term, the epidemic has forced governments to adopt different levels of lockdown measures. The total electricity consumption decreased and the share of renewable energy has increased. Based on the International Energy Agency's (IEA) data on electricity production and consumption, this paper analyzes the long-term effects: The COVID-19 pandemic does affect overall electricity demand and market structure in the short term, but in the long term, the impact of the pandemic is detrimental to the proportion of electricity generated from renewable sources. Increased, especially in hard-hit countries. The impact on countries with different levels of development is different, and countries with higher levels of economic development are better able to resist such exogenous shocks. The pandemic is still ongoing, how to steadily promote sustainable development? In this paper we suggest that to cope with climate change, to accomplish energy transformation and upgrading or even sustainable development, we should not only rely on reducing human activities, but give more support to countries with low development levels in realizing sustainable development.

关键词： COVID-19 Electricity generation Renewable energy

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Image Orientation Estimation Based On Deep Learning - A Survey

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Procedia Computer Science 2024年 242卷 1193-1197页

作者： Ruijie Xu Yong Shi Zhiquan Qi Research Center on Fictitious Economy and Data Science University of Chinese Academy of Sciences Beijing 100190 China Key Laboratory of Big Data Mining and Knowledge Management University of Chinese Academy of Sciences Beijing 00190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing 101408 China School of Economics and Management University of Chinese Academy of Sciences Beijing 100190 China College of Information Science and Technology University of Nebraska Omaha 68182 USA

In the realm of visual tasks, the estimation of orientation plays a pivotal role yet often remains an understudied aspect in the gamut of image analysis research. This paper endeavors to address this deficiency by conducting a comprehensive review of methods for orientation estimation within 2D imaging contexts. We aim to provide researchers with an exhaustive overview and detailed analysis of the techniques utilized for orientation estimation, thereby enhancing their understanding of this complex field. We introduce an organized classification of orientation estimation methods, segmented into approaches based on classification algorithms and those based on regression models. This classification not only sheds light on the distinct methodologies but also scrutinizes their underlying assumptions, practical applications. Additionally, the study identifies and underscores emerging research opportunities that hold the potential to enrich the discourse and technical advancements in orientation estimation, thereby encouraging deeper academic investigation and technological development.

关键词： Classification-based Orientation Estimation Regression-based Orientation Estimation

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